Radar systems used for air traffic control, as well as other purposes, need to deal with more than receiver noise in that they also receive echoes (i.e. clutter) from natural environmental conditions such as land, sea and weather. When appearing at the same radar cell as targets, clutter returns can block target detection since the clutter return may be several orders of magnitude larger than targets. One option for detecting moving targets in the midst of large clutter is to take advantage of the different Doppler shifts between the targets and clutter. A Moving Target Indicator (MTI) is an example of a technique that takes advantage of this property, and has been operated in the field for decades. A Moving Target Detector (MTD) is a more advanced successor of the MTI, and represents a significant advance in target detection in clutter. The MTD also has the ability to detect large tangential targets with the help of clutter maps. Since it was originally developed by MIT Lincoln Laboratory in the 1970s, the MTD has evolved through several generations.
In a conventional current generation MTD, a burst of pulses is transmitted at a constant pulse repetition frequency (PRF), returns from which are called a coherent processing interval (CPI) on reception. In general, the PRF is staggered from CPI to CPI in order to eliminate blind speed. A bank of Doppler filters is applied across pulses received in each CPI to separate moving targets from clutter. The output of each Doppler filter is processed by a Constant False Alarm Rate (CFAR) detector that uses a dynamic clutter map. The dynamic clutter map enables the detection of large tangential targets that would otherwise be canceled by an ordinary MTI, and also helps to control the false alarm breakthrough of the nonzero Doppler filters. Other maps such as a digital Sensitivity Time Control (STC) map and a geo-censor map can also be employed during either the detection stage or the post plot editing stage in order to control angel clutter and/or other geographic interference, as is commonly known by those skilled in the art. The detections from the CFAR detectors are merged, and the final detections are further integrated in a binary integrator before being sent to a plot extractor for azimuth and range centroiding. The binary integrator correlates the detections from several consecutive CPIs to control false alarms due to clutter or second time around targets. However, there is information loss in the binary integrator in that its inputs are detection results (0 or 1), which results in processing gain loss.